The present invention relates to a pattern recognition apparatus using a neural network system. This pattern recognition apparatus identifies patterns such as handwritten characters.
Technology relating to a pattern recognition apparatus using a neural network system has been disclosed in "Character recognition system using a neural network" (Yamada et. al.: Electronics Information Communications Association Bulletin PRU 88-58, pp. 79-86). This type of conventional technology uses a neural network of the layered type. This layered type of neural network has an input layer, an intermediate layer and an output layer. The output layer has the same number of output units as there are identification classes and each of the output units corresponds to one identification class. The identification class (also known as simply a "class") is information that specifies a pattern that can be identified. The learning for this neural network is performed so that when the neural network receives a predetermined learning pattern corresponding to each of identification classes, only the output unit corresponding to the identification class outputs "1" and the other output units output "0"s. In such a layered type of neural network, when an unknown pattern is applied, output values within a range of "0" or more and "1" or less appear in each output unit. Then, the identification class corresponding to the one of those output units in which the greatest output value appears, is the result of recognition with respect to that unknown pattern.
As has been described above, in a conventional pattern recognition apparatus using a layered type of neural network in which the output layer has a number of output units equal to the number of identification classes, increasing the number of identification classes (the number of patterns to be recognized) has the following disadvantages.
First, the total number of links in the neural network increases to make learning difficult.
Second, even output values differ greatly from the values that are desired, those output values are used as the basis for the judgment.
In addition, the identification class to which an input pattern belongs is judged by the greatest value for the output values appearing in each of the output units and so judgment to the effect that "the input pattern does not belong to any identification class" must be performed on the basis of a threshold value. Furthermore, when the number of identification classes increase, it is difficult to determine the threshold value.
In addition, in "Towards the construction of a large-scale neural network" (Mori; Electronics Information Communications Association Bulletin PRU 88-59, pp. 87-94), is proposed other technology relating to pattern recognition using a neural network.
In this conventional technology, a plural number of small-scale neural networks (sub-nets) and a neural network (a super-net) which inputs the outputs of these sub-nets are linked in layers. Then, the output layer of the super-net has a number of output units which is the same as the number of identification classes. As has been described above, conventional pattern recognition using a neural network that is the layered linking of a super-net and sub-nets as has been described above, has the following disadvantages. First, the information that is to be output from each of the sub-nets must be determined so that the desired values are obtained from the super-net. Second, the super-net becomes very large when the number of identification classes is increased and this makes learning of the super-net difficult.
Accordingly, it is currently difficult to configure a high-performance and large-scale neural network.
In addition, the performance of pattern recognition in accordance with the output values of a super-net and judgments such as "the input pattern does not belong to any identification class" must be performed on the basis of a threshold value, as has been described above.